Finding related tables from data lakes is an essential task in dataset discovery.
There are two sub-tasks in this application, namely finding joinable tables and table union search [1]}.
To support finding joinable tables, earlier studies utilized syntactic similarity metrics that are widely used in the applications of string similarity search and join [2]}, [3]}, [4]}.
LSH Ensemble used containment (overlap) [5]} as the similarity metric and provided a high-dimensional similarity search based solution.
Josie [6]} employed overlap over tokens and developed an exact data-optimized solution.
PEXESO [7]} relied on cosine similarity over word embeddings and proposed indexing techniques to improve performance.
The table union search problem has been well explored recently.
Ling et al. [8]} and Lehmberg et al. [9]} illustrated the importance of finding unionable tables.
Nargesian et al. [10]} proposed the first definition and comprehensive solution for the table union search problem.
Bogatu et al. [11]} proposed the \(D^3\) L system by dividing columns into different categories.
The SANTOS  [12]} system utilized knowledge base along with binary relationships in the data lake to identify tables that share unionable columns and relationships, and it is the state-of-the-art approach in this field.
To the best of our knowledge, our work is the first solution to utilize contrastive learning techniques in table union search.
